In [1]:
%tensorflow_version 2.x
import tensorflow
tensorflow.__version__
Out[1]:
'2.3.0'
In [2]:
%matplotlib inline
import pandas as pd
import numpy as np
import tensorflow as tf
from google.colab import files
import seaborn as sns
from sklearn.model_selection import train_test_split
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from sklearn import metrics
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score, precision_recall_curve, auc
uploaded = files.upload()
uploaded = files.upload()
/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving Labels.csv to Labels.csv
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving images.npy to images.npy

Pre-Processing Image Data

In [3]:
import numpy as np
Data = pd.read_csv("Labels.csv")
Data.shape
Out[3]:
(4750, 1)
In [4]:
img_array = np.load("images.npy", allow_pickle=True)
In [5]:
img_array.shape
Out[5]:
(4750, 128, 128, 3)
In [6]:
from matplotlib import pyplot as plt
fig, axes = plt.subplots(10, 10, figsize=(100,100))
for i, ax in enumerate(axes.flat):
  ax.imshow(img_array[i])

Visualizing of Images

In [7]:
X_data = np.array(img_array[:,:,0,0])
In [8]:
X_data.shape
Out[8]:
(4750, 128)
In [9]:
y_data = Data
In [10]:
y_data.shape
Out[10]:
(4750, 1)
In [11]:
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size = 0.3, random_state = 7)
In [12]:
from sklearn import preprocessing
X_train = preprocessing.normalize(X_train)
In [13]:
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
(3325, 128)
(1425, 128)
(3325, 1)
(1425, 1)
In [14]:
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

X_train /= 255
X_test /= 255
In [15]:
print("X_train shape:", X_train.shape)
print("Images in X_train:", X_train.shape[0])
print("Images in X_test:", X_test.shape[0])
print("Max value in X_train:", X_train.max())
print("Min value in X_train:", X_train.min())
X_train shape: (3325, 128)
Images in X_train: 3325
Images in X_test: 1425
Max value in X_train: 0.0014285049
Min value in X_train: 0.0
In [16]:
import cv2
from matplotlib import pyplot as plt
img_array = np.load("images.npy", allow_pickle=True)
fig, axes = plt.subplots(10, 10, figsize=(100,100))
for i, ax in enumerate(axes.flat):
  gaussian = cv2.GaussianBlur(img_array[i], (15, 15), 0)
  ax.imshow(gaussian)

Data Compatibility

In [17]:
from sklearn.preprocessing import LabelBinarizer
enc = LabelBinarizer()
y_train = enc.fit_transform(y_train)
y_test = enc.fit_transform(y_test)
print("Shape of y_train:", y_train.shape)
print("One value of y_train:", y_train[0])
Shape of y_train: (3325, 12)
One value of y_train: [0 0 0 1 0 0 0 0 0 0 0 0]
In [18]:
X_test, X_validation, y_test, y_validation = train_test_split(X_test, y_test, test_size = 0.5, random_state = 7)
In [19]:
validation_data = (X_validation, y_validation)
In [20]:
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
(3325, 128)
(712, 128)
(3325, 12)
(712, 12)
In [21]:
print(X_train[1])
[0.00057647 0.00054154 0.0005328  0.0005328  0.00051533 0.00048913
 0.00046293 0.00051533 0.0005328  0.00048913 0.00044546 0.00040178
 0.00041052 0.00042799 0.00042799 0.00040178 0.00039305 0.00034938
 0.00028824 0.00035811 0.00037558 0.00035811 0.00032317 0.00035811
 0.00036685 0.00037558 0.00038432 0.00038432 0.00039305 0.00036685
 0.00034938 0.00035811 0.00033191 0.00036685 0.00033191 0.00033191
 0.00029697 0.0002795  0.00027077 0.00026203 0.0002795  0.00028824
 0.00028824 0.00034938 0.00041052 0.00047166 0.00047166 0.00038432
 0.00019216 0.00020089 0.00030571 0.00032317 0.00030571 0.00032317
 0.00033191 0.00032317 0.00040178 0.00033191 0.00028824 0.00032317
 0.00032317 0.00032317 0.00031444 0.00031444 0.00031444 0.00030571
 0.00029697 0.00030571 0.00029697 0.00031444 0.00043672 0.00048039
 0.00037558 0.00033191 0.00031444 0.00030571 0.00028824 0.00028824
 0.0002533  0.00023583 0.00026203 0.0002795  0.00027077 0.0002271
 0.0002271  0.0002271  0.0002271  0.00023583 0.00023583 0.0002533
 0.0002533  0.00024456 0.0002271  0.00023583 0.0002795  0.00030571
 0.00035811 0.00039305 0.00035811 0.0002795  0.00034938 0.00029697
 0.00027077 0.00034938 0.00031444 0.00032317 0.00021836 0.00020963
 0.00026203 0.00032317 0.00031444 0.00028824 0.00033191 0.00035811
 0.00034938 0.00029697 0.0002533  0.00026203 0.00026203 0.00034064
 0.00038432 0.00038432 0.00039305 0.00041925 0.00033191 0.00028824
 0.00034064 0.00032317]
In [22]:
X_train = X_train.reshape((X_train.shape[0], 128)).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 128).astype('float32')

print(X_train.shape)
print(X_test.shape)
(3325, 128)
(712, 128)
In [23]:
X_train = np.expand_dims(X_train, axis = 2)
print(X_train.shape)
print(X_test.shape)
(3325, 128, 1)
(712, 128)

Building CNN

In [24]:
from tensorflow.keras import datasets, models, layers, optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from google.colab.patches import cv2_imshow
In [25]:
# Set the CNN model

batch_size = None

model = models.Sequential()
model.add(layers.Conv2D(32, (5, 5), padding='same', activation="relu", input_shape=(128,128,1),data_format='channels_first'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.2))
model.add(layers.Conv2D(64, (5, 5), padding='same', activation="relu"))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.3))
model.add(layers.Conv2D(64, (3, 3), padding='same', activation="relu"))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.4))
model.add(layers.Conv2D(64, (3, 3), padding='same', activation="relu"))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.5))

model.add(layers.GlobalMaxPooling2D())
model.add(layers.Dense(256, activation="relu"))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(10, activation="softmax"))

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 32, 128, 1)        102432    
_________________________________________________________________
batch_normalization (BatchNo (None, 32, 128, 1)        4         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 16, 64, 1)         0         
_________________________________________________________________
dropout (Dropout)            (None, 16, 64, 1)         0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 16, 64, 64)        1664      
_________________________________________________________________
batch_normalization_1 (Batch (None, 16, 64, 64)        256       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 8, 32, 64)         0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 8, 32, 64)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 8, 32, 64)         36928     
_________________________________________________________________
batch_normalization_2 (Batch (None, 8, 32, 64)         256       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 4, 16, 64)         0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 4, 16, 64)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 4, 16, 64)         36928     
_________________________________________________________________
batch_normalization_3 (Batch (None, 4, 16, 64)         256       
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 2, 8, 64)          0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 2, 8, 64)          0         
_________________________________________________________________
global_max_pooling2d (Global (None, 64)                0         
_________________________________________________________________
dense (Dense)                (None, 256)               16640     
_________________________________________________________________
dropout_4 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                2570      
=================================================================
Total params: 197,934
Trainable params: 197,548
Non-trainable params: 386
_________________________________________________________________
In [26]:
opt = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
In [27]:
model.compile(loss='categorical_crossentropy',
              optimizer=opt,
              metrics=['accuracy'])
In [28]:
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 32, 128, 1)        102432    
_________________________________________________________________
batch_normalization (BatchNo (None, 32, 128, 1)        4         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 16, 64, 1)         0         
_________________________________________________________________
dropout (Dropout)            (None, 16, 64, 1)         0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 16, 64, 64)        1664      
_________________________________________________________________
batch_normalization_1 (Batch (None, 16, 64, 64)        256       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 8, 32, 64)         0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 8, 32, 64)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 8, 32, 64)         36928     
_________________________________________________________________
batch_normalization_2 (Batch (None, 8, 32, 64)         256       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 4, 16, 64)         0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 4, 16, 64)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 4, 16, 64)         36928     
_________________________________________________________________
batch_normalization_3 (Batch (None, 4, 16, 64)         256       
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 2, 8, 64)          0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 2, 8, 64)          0         
_________________________________________________________________
global_max_pooling2d (Global (None, 64)                0         
_________________________________________________________________
dense (Dense)                (None, 256)               16640     
_________________________________________________________________
dropout_4 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                2570      
=================================================================
Total params: 197,934
Trainable params: 197,548
Non-trainable params: 386
_________________________________________________________________

Evaluate the model.

Hi, last night, my model.fit function stopped working all of a sudden. I am not able to make even the mentor session examples work. In order for my code to compile, I am using a really simple model. This is my original code: model1.fit( x = X_train, y=y_train, batch_size=128, epochs=10, validation_split = 0.5). It worked up until some point, and then it is not. I tried classroom examples too and am getting errors. I can't make this code work with the simplified model: scores = model2.evaluate(x, y, verbose=1) print('Test loss:', scores[0]) print('Test accuracy:', scores[1]).

In [29]:
model2 = Sequential()
model2.add(Dense(1, input_shape=(1,)))
model2.compile(loss='mse', optimizer='rmsprop')

# The fit() method - trains the model
x = np.random.uniform(0.0, 1.0, (200))
y = 0.3 + 0.6*x + np.random.normal(0.0, 0.05,(200))
model2.fit(x, y, epochs=1000, batch_size=100)
Epoch 1/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.7606
Epoch 2/1000
2/2 [==============================] - 0s 998us/step - loss: 0.7483
Epoch 3/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7396
Epoch 4/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.7323
Epoch 5/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7257
Epoch 6/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.7195
Epoch 7/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7137
Epoch 8/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.7081
Epoch 9/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7027
Epoch 10/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6975
Epoch 11/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6923
Epoch 12/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6873
Epoch 13/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6823
Epoch 14/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6773
Epoch 15/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6725
Epoch 16/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6676
Epoch 17/1000
2/2 [==============================] - 0s 989us/step - loss: 0.6628
Epoch 18/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6581
Epoch 19/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6533
Epoch 20/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6486
Epoch 21/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6440
Epoch 22/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6393
Epoch 23/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6347
Epoch 24/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6301
Epoch 25/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6255
Epoch 26/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6210
Epoch 27/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6164
Epoch 28/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6119
Epoch 29/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6074
Epoch 30/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6029
Epoch 31/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5985
Epoch 32/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.5940
Epoch 33/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5896
Epoch 34/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5852
Epoch 35/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5808
Epoch 36/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5764
Epoch 37/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5720
Epoch 38/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.5677
Epoch 39/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5634
Epoch 40/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5591
Epoch 41/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5548
Epoch 42/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5505
Epoch 43/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.5463
Epoch 44/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5421
Epoch 45/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5378
Epoch 46/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5336
Epoch 47/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5295
Epoch 48/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5253
Epoch 49/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5212
Epoch 50/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5170
Epoch 51/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5129
Epoch 52/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5088
Epoch 53/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5048
Epoch 54/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5007
Epoch 55/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4967
Epoch 56/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4927
Epoch 57/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4887
Epoch 58/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4847
Epoch 59/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4807
Epoch 60/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4768
Epoch 61/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4728
Epoch 62/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4689
Epoch 63/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4650
Epoch 64/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4611
Epoch 65/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4573
Epoch 66/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4534
Epoch 67/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4496
Epoch 68/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4458
Epoch 69/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4420
Epoch 70/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4382
Epoch 71/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4345
Epoch 72/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4307
Epoch 73/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4270
Epoch 74/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4233
Epoch 75/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4196
Epoch 76/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4160
Epoch 77/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4123
Epoch 78/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4087
Epoch 79/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4051
Epoch 80/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.4015
Epoch 81/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3979
Epoch 82/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3943
Epoch 83/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3908
Epoch 84/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3873
Epoch 85/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3838
Epoch 86/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3803
Epoch 87/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3768
Epoch 88/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3734
Epoch 89/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3699
Epoch 90/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3665
Epoch 91/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3631
Epoch 92/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3597
Epoch 93/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3564
Epoch 94/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3530
Epoch 95/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3497
Epoch 96/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3464
Epoch 97/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3431
Epoch 98/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3398
Epoch 99/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3365
Epoch 100/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3333
Epoch 101/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3300
Epoch 102/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3268
Epoch 103/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3236
Epoch 104/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3204
Epoch 105/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3173
Epoch 106/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3141
Epoch 107/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3110
Epoch 108/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3079
Epoch 109/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3048
Epoch 110/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3017
Epoch 111/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2987
Epoch 112/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2956
Epoch 113/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2926
Epoch 114/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2896
Epoch 115/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2866
Epoch 116/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2837
Epoch 117/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2807
Epoch 118/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2778
Epoch 119/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2749
Epoch 120/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2720
Epoch 121/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2691
Epoch 122/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2662
Epoch 123/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2634
Epoch 124/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2605
Epoch 125/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2577
Epoch 126/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2549
Epoch 127/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2522
Epoch 128/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2494
Epoch 129/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2467
Epoch 130/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2440
Epoch 131/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2412
Epoch 132/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2386
Epoch 133/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2359
Epoch 134/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2332
Epoch 135/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2306
Epoch 136/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2280
Epoch 137/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2254
Epoch 138/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2228
Epoch 139/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2202
Epoch 140/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2177
Epoch 141/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2151
Epoch 142/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2126
Epoch 143/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2101
Epoch 144/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2076
Epoch 145/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.2052
Epoch 146/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2027
Epoch 147/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2003
Epoch 148/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1979
Epoch 149/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1955
Epoch 150/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.1931
Epoch 151/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1908
Epoch 152/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1884
Epoch 153/1000
2/2 [==============================] - 0s 958us/step - loss: 0.1861
Epoch 154/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1838
Epoch 155/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1815
Epoch 156/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1792
Epoch 157/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1770
Epoch 158/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1747
Epoch 159/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1725
Epoch 160/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1703
Epoch 161/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1681
Epoch 162/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1659
Epoch 163/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1638
Epoch 164/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1616
Epoch 165/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.1595
Epoch 166/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.1574
Epoch 167/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.1553
Epoch 168/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1533
Epoch 169/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1512
Epoch 170/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1492
Epoch 171/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1472
Epoch 172/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1452
Epoch 173/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1432
Epoch 174/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1412
Epoch 175/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1393
Epoch 176/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1373
Epoch 177/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.1354
Epoch 178/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1336
Epoch 179/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1317
Epoch 180/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1298
Epoch 181/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1280
Epoch 182/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1262
Epoch 183/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1244
Epoch 184/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1226
Epoch 185/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1208
Epoch 186/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1190
Epoch 187/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1173
Epoch 188/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1156
Epoch 189/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1139
Epoch 190/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1122
Epoch 191/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1105
Epoch 192/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1088
Epoch 193/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1072
Epoch 194/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1056
Epoch 195/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1040
Epoch 196/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1024
Epoch 197/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1009
Epoch 198/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0993
Epoch 199/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0978
Epoch 200/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0963
Epoch 201/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0948
Epoch 202/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0933
Epoch 203/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0918
Epoch 204/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0904
Epoch 205/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0889
Epoch 206/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0875
Epoch 207/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0861
Epoch 208/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0847
Epoch 209/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0834
Epoch 210/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0820
Epoch 211/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0807
Epoch 212/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0794
Epoch 213/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0781
Epoch 214/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0768
Epoch 215/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0755
Epoch 216/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0743
Epoch 217/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0731
Epoch 218/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0718
Epoch 219/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0706
Epoch 220/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0695
Epoch 221/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0683
Epoch 222/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0672
Epoch 223/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0660
Epoch 224/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0649
Epoch 225/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0638
Epoch 226/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0627
Epoch 227/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0617
Epoch 228/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0606
Epoch 229/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0596
Epoch 230/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0586
Epoch 231/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0576
Epoch 232/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0566
Epoch 233/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0556
Epoch 234/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0547
Epoch 235/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0538
Epoch 236/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0528
Epoch 237/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0520
Epoch 238/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0511
Epoch 239/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0502
Epoch 240/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0494
Epoch 241/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0485
Epoch 242/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0477
Epoch 243/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0469
Epoch 244/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0461
Epoch 245/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0454
Epoch 246/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0446
Epoch 247/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0439
Epoch 248/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0432
Epoch 249/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0425
Epoch 250/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0418
Epoch 251/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0411
Epoch 252/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0404
Epoch 253/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0398
Epoch 254/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0392
Epoch 255/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0386
Epoch 256/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0380
Epoch 257/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0374
Epoch 258/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0368
Epoch 259/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0363
Epoch 260/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0357
Epoch 261/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0352
Epoch 262/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0347
Epoch 263/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0342
Epoch 264/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0338
Epoch 265/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0333
Epoch 266/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0329
Epoch 267/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0324
Epoch 268/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0320
Epoch 269/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0316
Epoch 270/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0312
Epoch 271/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0309
Epoch 272/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0305
Epoch 273/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0301
Epoch 274/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0298
Epoch 275/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0295
Epoch 276/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0292
Epoch 277/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0289
Epoch 278/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0286
Epoch 279/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0284
Epoch 280/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0281
Epoch 281/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0279
Epoch 282/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0276
Epoch 283/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0274
Epoch 284/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0272
Epoch 285/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0270
Epoch 286/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0268
Epoch 287/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0266
Epoch 288/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0264
Epoch 289/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0262
Epoch 290/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0260
Epoch 291/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0259
Epoch 292/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0257
Epoch 293/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0255
Epoch 294/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0254
Epoch 295/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0252
Epoch 296/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0251
Epoch 297/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0249
Epoch 298/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0247
Epoch 299/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0246
Epoch 300/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0245
Epoch 301/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0243
Epoch 302/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0241
Epoch 303/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0239
Epoch 304/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0238
Epoch 305/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0236
Epoch 306/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0235
Epoch 307/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0233
Epoch 308/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0231
Epoch 309/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0230
Epoch 310/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0228
Epoch 311/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0226
Epoch 312/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0225
Epoch 313/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0223
Epoch 314/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0221
Epoch 315/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0220
Epoch 316/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0218
Epoch 317/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0217
Epoch 318/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0215
Epoch 319/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0214
Epoch 320/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0212
Epoch 321/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0210
Epoch 322/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0209
Epoch 323/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0207
Epoch 324/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0206
Epoch 325/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0205
Epoch 326/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0203
Epoch 327/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0201
Epoch 328/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0200
Epoch 329/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0198
Epoch 330/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0197
Epoch 331/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0195
Epoch 332/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0194
Epoch 333/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0192
Epoch 334/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0191
Epoch 335/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0189
Epoch 336/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0188
Epoch 337/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0186
Epoch 338/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0185
Epoch 339/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0184
Epoch 340/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0182
Epoch 341/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0181
Epoch 342/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0179
Epoch 343/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0178
Epoch 344/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0176
Epoch 345/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0175
Epoch 346/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0174
Epoch 347/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0172
Epoch 348/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0171
Epoch 349/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0169
Epoch 350/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0168
Epoch 351/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0167
Epoch 352/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0165
Epoch 353/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0164
Epoch 354/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0163
Epoch 355/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0161
Epoch 356/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0160
Epoch 357/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0159
Epoch 358/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0157
Epoch 359/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0156
Epoch 360/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0155
Epoch 361/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0153
Epoch 362/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0152
Epoch 363/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0151
Epoch 364/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0149
Epoch 365/1000
2/2 [==============================] - 0s 10ms/step - loss: 0.0148
Epoch 366/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0147
Epoch 367/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0146
Epoch 368/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0144
Epoch 369/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0143
Epoch 370/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0142
Epoch 371/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0141
Epoch 372/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0140
Epoch 373/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0138
Epoch 374/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0137
Epoch 375/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0136
Epoch 376/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0135
Epoch 377/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0133
Epoch 378/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0132
Epoch 379/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0131
Epoch 380/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0130
Epoch 381/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0129
Epoch 382/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0128
Epoch 383/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0126
Epoch 384/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0125
Epoch 385/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0124
Epoch 386/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0123
Epoch 387/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0122
Epoch 388/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0121
Epoch 389/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0120
Epoch 390/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0119
Epoch 391/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0117
Epoch 392/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0116
Epoch 393/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0115
Epoch 394/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0114
Epoch 395/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0113
Epoch 396/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0112
Epoch 397/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0111
Epoch 398/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0110
Epoch 399/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0109
Epoch 400/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0108
Epoch 401/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0107
Epoch 402/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0106
Epoch 403/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0105
Epoch 404/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0104
Epoch 405/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0103
Epoch 406/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0102
Epoch 407/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0101
Epoch 408/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0100
Epoch 409/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0099
Epoch 410/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0098
Epoch 411/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0097
Epoch 412/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0096
Epoch 413/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0095
Epoch 414/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0094
Epoch 415/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0093
Epoch 416/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0092
Epoch 417/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0091
Epoch 418/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0090
Epoch 419/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0089
Epoch 420/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0088
Epoch 421/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0087
Epoch 422/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0087
Epoch 423/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0086
Epoch 424/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0085
Epoch 425/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0084
Epoch 426/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0083
Epoch 427/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0082
Epoch 428/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0081
Epoch 429/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0080
Epoch 430/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0079
Epoch 431/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0079
Epoch 432/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0078
Epoch 433/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0077
Epoch 434/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0076
Epoch 435/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0075
Epoch 436/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0075
Epoch 437/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0074
Epoch 438/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0073
Epoch 439/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0072
Epoch 440/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0071
Epoch 441/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0071
Epoch 442/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0070
Epoch 443/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0069
Epoch 444/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0068
Epoch 445/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0068
Epoch 446/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0067
Epoch 447/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0066
Epoch 448/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0066
Epoch 449/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0065
Epoch 450/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0064
Epoch 451/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0063
Epoch 452/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0063
Epoch 453/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0062
Epoch 454/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0061
Epoch 455/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0061
Epoch 456/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0060
Epoch 457/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0059
Epoch 458/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0059
Epoch 459/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0058
Epoch 460/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0058
Epoch 461/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0057
Epoch 462/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0056
Epoch 463/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0056
Epoch 464/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0055
Epoch 465/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0055
Epoch 466/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0054
Epoch 467/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0053
Epoch 468/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0053
Epoch 469/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0052
Epoch 470/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0051
Epoch 471/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0051
Epoch 472/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0050
Epoch 473/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0050
Epoch 474/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0049
Epoch 475/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0049
Epoch 476/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0048
Epoch 477/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0048
Epoch 478/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0047
Epoch 479/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0047
Epoch 480/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0046
Epoch 481/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0045
Epoch 482/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0045
Epoch 483/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0044
Epoch 484/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0044
Epoch 485/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0043
Epoch 486/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0043
Epoch 487/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0043
Epoch 488/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0042
Epoch 489/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0042
Epoch 490/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0041
Epoch 491/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0041
Epoch 492/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0040
Epoch 493/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0040
Epoch 494/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 495/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 496/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 497/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0038
Epoch 498/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 499/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 500/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 501/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 502/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 503/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 504/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0035
Epoch 505/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 506/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 507/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0034
Epoch 508/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 509/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 510/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0033
Epoch 511/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 512/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 513/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 514/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 515/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 516/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 517/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 518/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 519/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 520/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 521/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 522/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 523/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 524/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 525/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 526/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 527/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 528/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 529/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 530/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 531/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 532/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 533/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 534/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 535/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 536/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 537/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 538/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 539/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 540/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 541/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 542/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 543/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 544/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 545/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 546/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 547/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 548/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 549/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 550/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 551/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 552/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 553/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 554/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 555/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 556/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 557/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 558/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 559/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 560/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 561/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 562/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 563/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 564/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 565/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 566/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 567/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 568/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 569/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 570/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 571/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 572/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 573/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 574/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 575/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 576/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 577/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 578/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 579/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 580/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 581/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 582/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 583/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 584/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 585/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 586/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 587/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 588/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 589/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 590/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 591/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 592/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 593/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 594/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 595/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 596/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 597/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 598/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 599/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 600/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 601/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 602/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 603/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 604/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 605/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 606/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 607/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 608/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 609/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 610/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 611/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 612/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 613/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 614/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 615/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 616/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 617/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 618/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 619/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 620/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 621/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 622/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 623/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 624/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 625/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 626/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 627/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 628/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 629/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 630/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 631/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 632/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 633/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 634/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 635/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 636/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 637/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 638/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 639/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 640/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 641/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 642/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 643/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 644/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 645/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 646/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 647/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 648/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 649/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 650/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 651/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 652/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 653/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 654/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 655/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 656/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 657/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 658/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 659/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 660/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 661/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 662/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 663/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 664/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 665/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 666/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 667/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 668/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 669/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 670/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 671/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 672/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 673/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 674/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 675/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 676/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 677/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 678/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 679/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 680/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 681/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 682/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 683/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 684/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 685/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 686/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 687/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 688/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 689/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 690/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 691/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 692/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 693/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 694/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 695/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 696/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 697/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 698/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 699/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 700/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 701/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 702/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 703/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 704/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 705/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 706/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 707/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 708/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 709/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 710/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 711/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 712/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 713/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 714/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 715/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 716/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 717/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 718/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 719/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 720/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 721/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 722/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 723/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 724/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 725/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 726/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 727/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 728/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 729/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 730/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 731/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 732/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 733/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 734/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 735/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 736/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 737/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 738/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 739/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 740/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 741/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 742/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 743/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 744/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 745/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 746/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 747/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 748/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 749/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 750/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 751/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 752/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 753/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 754/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 755/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 756/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 757/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 758/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 759/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 760/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 761/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 762/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 763/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 764/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 765/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 766/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 767/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 768/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 769/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 770/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 771/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 772/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 773/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 774/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 775/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 776/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 777/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 778/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 779/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 780/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 781/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 782/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 783/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 784/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 785/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 786/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 787/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 788/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 789/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 790/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 791/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 792/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 793/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 794/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 795/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 796/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 797/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 798/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 799/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 800/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 801/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 802/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 803/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 804/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 805/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 806/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 807/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 808/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 809/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 810/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 811/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 812/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 813/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 814/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 815/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 816/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 817/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 818/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 819/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 820/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 821/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 822/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 823/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 824/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 825/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 826/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 827/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 828/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 829/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 830/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 831/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 832/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 833/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 834/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 835/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 836/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 837/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 838/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 839/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 840/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 841/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 842/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 843/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 844/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 845/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 846/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 847/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 848/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 849/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 850/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 851/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 852/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 853/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 854/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 855/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 856/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 857/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 858/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 859/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 860/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 861/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 862/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 863/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 864/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 865/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 866/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 867/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 868/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 869/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 870/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 871/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 872/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 873/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 874/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 875/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 876/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 877/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 878/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 879/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 880/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 881/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 882/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 883/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 884/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 885/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 886/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 887/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 888/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 889/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 890/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 891/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 892/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 893/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 894/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 895/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 896/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 897/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 898/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 899/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 900/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 901/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 902/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 903/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 904/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 905/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 906/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 907/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 908/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 909/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 910/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 911/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 912/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 913/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 914/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 915/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 916/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 917/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 918/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 919/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 920/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 921/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 922/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 923/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 924/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 925/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 926/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 927/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 928/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 929/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 930/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 931/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 932/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 933/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 934/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 935/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 936/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 937/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 938/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 939/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 940/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 941/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 942/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 943/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 944/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 945/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 946/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 947/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 948/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 949/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 950/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 951/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 952/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 953/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 954/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 955/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 956/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 957/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 958/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 959/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 960/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 961/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 962/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 963/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 964/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 965/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 966/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 967/1000
2/2 [==============================] - 0s 7ms/step - loss: 0.0024
Epoch 968/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 969/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 970/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 971/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 972/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 973/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 974/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 975/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 976/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 977/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 978/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 979/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 980/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 981/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 982/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 983/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 984/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 985/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 986/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 987/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 988/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 989/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 990/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 991/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 992/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 993/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 994/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 995/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 996/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 997/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 998/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 999/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1000/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Out[29]:
<tensorflow.python.keras.callbacks.History at 0x7fe8faf85f98>
In [30]:
results = model2.evaluate(x, y)
Y_pred_cls = model2.predict_classes(y, batch_size=200, verbose=0)
print('Accuracy Model (Dropout): '+ str(model2.evaluate(x,y)))
print('Recall_score: ' + str(recall_score(Y_pred_cls, Y_pred_cls)))
print('Precision_score: ' + str(precision_score(Y_pred_cls, Y_pred_cls)))
print('F-score: ' + str(f1_score(Y_pred_cls,Y_pred_cls)))
conf = confusion_matrix(Y_pred_cls, Y_pred_cls)
sns.heatmap(conf.T, square=True, annot=True, cbar=False, cmap=plt.cm.Blues)
plt.xlabel('Predicted Values')
plt.ylabel('True Values');
plt.show();
7/7 [==============================] - 0s 1ms/step - loss: 0.0024
WARNING:tensorflow:From <ipython-input-30-c2411fccecb2>:2: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.
Instructions for updating:
Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
7/7 [==============================] - 0s 1ms/step - loss: 0.0024
Accuracy Model (Dropout): 0.002388233318924904
Recall_score: 1.0
Precision_score: 1.0
F-score: 1.0
In [31]:
from keras.utils.vis_utils import plot_model
plot_model(model2, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
Out[31]:
In [32]:
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.9, random_state=0)
y_pred = model2.predict(X_test)
import numpy as np
from matplotlib import pyplot as plt

data = np.array([
    [X_test[2], y_pred[2]],
    [X_test[3], y_pred[3]],
    [X_test[33], y_pred[33]],
    [X_test[36], y_pred[36]],
    [X_test[59], y_pred[59]],
])
x, y = data.T
plt.scatter(x,y)
plt.show()